Tom Graeber, PhD
Thomas Graeber is faculty in the Department of Molecular and Medical Pharmacology and a member of the Crump Institute for Molecular Imaging at UCLA, and is a National Human Genome Research Institute (NHGRI) Genome Scholar. His background includes physics, cancer biology, signal transduction, computational biology and proteomics, and he is now building experimental and computational approaches to studying cancer signaling from a systems perspective.
His work in cancer biology started with the discovery that hypoxia, a common feature of solid tumors, induces p53 protein levels, and that p53 deficient cells are less prone to undergo apoptosis in low oxygen conditions, conferring a survival advantage. These findings led to a model of hypoxia as a physiological selective force against apoptosis-competent cells in developing tumors, thus explaining the previously unaccounted for high frequency of p53 mutations in cancer. In computational biology, he developed an algorithm to identify potential autocrine signaling loops in cancer using gene expression microarray data. The algorithm integrates biological data (in this case, cognate ligand-receptor partners) into the analysis of raw gene expression data, and a number of leads from this method have been verified to play critical roles in cell signaling.
Recently, his lab has developed a mass-spectrometry based protocol for identifying tyrosine-phosphorylated proteins from cancer cell lysates. They are using this proteome-wide 'phosphorylation profiling' assay to identify the signaling pathways activated by various oncogenic initiating events (e.g. kinase mutations), and to elucidate the interconnectedness of classical signaling pathways into a more comprehensive signaling network. His lab is also analyzing large gene expression and proteomics datasets on human prostate cancer and mouse models of prostate cancer, and developing bioinformatic algorithms to identify conserved/critical oncogenic mechanisms through cross-species comparisons.
In modeling cancer signaling, one of the lab’s goals is to identify minimal sets of informative components that best reflect the state of the cell and serve as molecular targets for nanodevice-based diagnostics, PET imaging, and patient-tailored treatment.